Geolocation tracking to monitor spatial distribution and habitat selection of cows, horses and sheep grazing in mountainous areas

Abstract

Precision Livestock Farming (PLF) technologies offer an opportunity to monitor livestock, enhancing farmers’ decision-making for improved control, better animal performance, and reduced environmental impact through proper management of pasture areas. The objective of this study was to assess the potential of data provided by commercial geolocation collars, along with open data resources such as information on natural habitats, topography, and vegetation, to detect grazing preferences of mountain livestock. We monitored 240 animals from three different herds and species (140 cows, 50 horses, and 50 sheep) during the grazing season (6 months) using geolocation collars in the Alt Pirineu Natural Park (80,000 ha), located in Catalonia, Spain. Animal distributions were analysed spatially and temporally across different seasonal periods (Spring: May-Jun, Summer: Jul-Aug and Autumn: Sep-Oct). Geolocation data were used to assess livestock preferences and avoidances regarding different types of terrain, land cover, and vegetation, estimated using Jacob’s selection index (JSI), a metric indicating whether animals use a particular area more or less than expected based on its availability. Additionally, we examined the influence of these environmental factors and the distance to water sources on animal distribution, and we identified high-density grazing hotspots. Results indicated that cows and horses positively selected areas with lower altitudes (JSI = 0.29 and 0.17, p < 0.05) and gentler slopes (JSI = 0.38 and 0.22, p < 0.05), whereas sheep preferred higher altitudes (JSI = 0.10, p < 0.05). Only cows showed a preference for areas with bare or dispersed vegetation. In general, all three species selected land covers such as open forests, meadows, wetlands, and water points, but changed depending on the season and species. The distance to water was greater for cows and sheep, particularly during the summer, whereas only horses showed a strong dependence on proximity to water sources. Finally, we identified and compared high-density grazing hotspots among the three species. These findings reveal not only interesting heterogeneity in distribution patterns among species sharing the same area, but also clear seasonal differences. In conclusion, data automatically collected from geolocation collars demonstrate strong potential for studying livestock grazing preferences, particularly in remote or hard-to-access mountainous areas. This information improves our understanding of livestock-environment interactions without requiring physical presence and can be effectively applied to support extensive grazing management.

Simple summary

Precision Livestock Farming (PLF) technologies, such as geolocation collars, help farmers monitor animals more effectively and manage pastures sustainably. In this study, we tracked 240 animals (cows, horses, and sheep) in a mountainous natural park in Catalonia, Spain, over six months. Using geolocation data combined with information about the landscape, we analysed where and when different species preferred to graze. We found that cows and horses tend to use lower, gentler areas, while sheep prefer higher ground. All species avoid dense forests, favouring open habitats like meadows and wetlands, with seasonal and vegetation-based variations in preference. Horses stay closer to water sources due to their fibrous diet, while cows and sheep showed less dependence on water proximity. Riparian zones and fens, though highly attractive to large grazers, are ecologically sensitive and require careful management, such as selective fencing. Sheep was the most diet selective, with horses being the least. These insights suggest that tailored grazing strategies, such as species rotation, habitat-based planning, and multi-species-grazing, can optimize pasture use while protecting fragile ecosystems. These insights highlight how geolocation tracking can improve livestock management in remote mountain areas, helping farmers make better decisions and improving conservation.